Wednesday, August 19, 2015

Session 5 Paper 1: Laissez-Faire: Fully asymmetric backscatter communication

Authors: Pan Hu (University of Massachusetts Amherst), Pengyu Zhang (University of Massachusetts Amherst), Deepak Ganesan (University of Massachusetts Amherst)

Presenter: Pan Hu

Link to the public review

This talk presents the design of an asymmetric backscatter system, the Laissez-Faire (LF-Backscatter). Existing backscatter systems usually suffer from the tradeoff between energy-efficient and available data rate. The presented LF-Backscatter in this talk introduces a protocol that can enable nodes to blindly transmit data when they sense. The proposed system targets at both achieving the high energy-efficient and high data rate. The system adopts a extremely power-efficient backscatter radio. The backscatter RX only needs one transistor. Nodes can transmit at the same time and can transmit whenever they want to.

One difficulty in designing the system is the decoding multiple concurrent transmissions at the readers. To solve this problem, this work introduces method to separate interleaved signals in the time domain, and separate the collisions of transmissions in the frequency domain. The edges of the received signals from the tags can be detected at the reader, where the reader adopts a high sampling frequency.


The proposed system is implemented and evaluated. Experimental results show that LF-backscatter can improve the data rate by 15 times compared to TDMA. Furthermore, LF-backscatter can also improve the energy efficiency by 20 times when compared with TDMA and Buzz. One drawback of this system is that it requires a high SNR, typically 4dB higher than the other two techniques.


Q: The capacity of the system?
A: Can even achieve 100kbps with several dozens of nodes.

Q: You claim at the beginning that the tags can work at bitrate and at any time. Is that ture?
A: The current bottleneck is the transmission from the Reader to the Tags, not from Tags to the Readers. A requirement of 4dB higher SNR is not bad.